Do Political Parties Matter? Evidence from U.S. Cities∗

January 15, 2007

Fernando Ferreira Joseph Gyourko The Wharton School The Wharton School University of Pennsylvania University of Pennsylvania

& NBER

Abstract

Partisan political differences have important effects on policy outcomes at the state and federal levels of government. We reexamine this issue at the local level using a new panel data set of mayoral elections in the United States. Applying a regression discontinuity design to deal with the endogeneity of the incumbent party, we find that party labels do not affect key fiscal policy outcomes of municipal governments, even though there is a large advantage to incumbency in terms of the probability of winning the next election and the margin of victory. The absence of a strong partisan impact at the local level appears due to two factors: (a) narrower differences in the parties’ preferences regarding policies relevant to city government; and (b) Tiebout-related forces provide the proper incentives for local politicians to be able to credibly commit to moderation.

∗ The authors thank the Research Sponsor Program of the Zell/Lurie Real Estate Center at Wharton for financial support. Misha Dworsky, Andrew Moore, Bob Jobim and Igar Fuki provided outstanding research assistance. We also appreciate the comments and suggestions of Claudio Ferraz, Ed Glaeser, Bob Inman, and seminar participants at the Wharton Applied Economics Workshop, Columbia University, Duke University, the Federal Reserve Bank of Philadelphia and IPEA – Rio de Janeiro.

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I. Introduction

Much recent research concludes that political partisanship influences various policy and

economic outcomes at the state and national levels of government.1 In this paper, we analyze

whether political parties matter in a similar sense at the municipal level of government. Not only is

the local public sector large in economic terms and responsible for many essential services2, but the

environment in which city governments operate is different in important ways that could affect the

influence of partisan politics. For example, the nature of city spending and the scope of services

provided could be such that the parties’ preferences regarding issues relevant to local government

may not be that far apart. In addition, the competitive environment in which local governments

operate certainly is much different from that faced by state and national governments. Tiebout-like

forces leading to the formation of relatively homogenous communities and the high mobility of

households within a metropolitan area could provide conditions to constrain partisanship at the

local level.

Data limitations, not any lack of interest, account for the absence of studies on the impact of

political partisanship at the city level in the United States.3 The key drawback has been the absence

of a large electoral data base at the municipal level. This deficit is eliminated by a new mayoral

elections data set we collected for this paper. The sample includes information on more than 3,000

mayoral races between 1950 and 2005 in approximately 400 cities with populations of at least 25,000

residents as of the year 2000. These data are used to analyze the impact of the two national political

parties - specifically, whether the mayor is a Democrat or Republican - on local policy outcomes

such as the size of local government and the composition of local public expenditures. Our study

1 There is now a consensus that U.S. congressional voting behavior is highly partisan (e.g., Poole and Rosenthal (1984), Snyder and Groseclose (2000), and Lee, Moretti, and Butler (2004)). Snowberg, Wolfers and Zitzewitz (2007) report that political party control matters for the executive branch of the federal government, too, by showing that the probability of electing a Democrat versus a Republican President (as observed in prediction markets) influences the value of financial market indexes. At the state level, Besley and Case (2003) find that partisanship affects key fiscal measures such as the size of government and welfare-related expenses. Their review of the literature also notes several studies with similar findings (e.g., Grogan (1994), Besley and Case (1995), Knight (2000), and Rogers and Rogers (2000)). 2 The latest Annual Survey of Governments shows local government full-time employment to be nearly 12 million, or almost three times the number working for the fifty state governments, and total local government revenues in excess of $1.2 trillion. These figures include data on counties, cities, townships and school districts. Cities and townships which constitute the municipalities that are our unit of observation account for approximately one-third of those totals. While we use the terms local and municipal government interchangeably, our data are for municipalities exclusively. 3 There are recent papers that study the impact of local politics abroad. Bertrand and Kramarz (2002) analyze the influence of parties on local zoning boards in France, and their effect on the long-term growth of regional income and employment. Ferraz and Finan (2005) look at the political determinants of corruption in cities in Brazil, and Pettersson-Lidbom (2006) shows that party labels matter for local policies in Sweden. Levitt (1997) is an example of related work on the U.S, in which he estimates the impact of local electoral cycles on investments in public safety and the subsequent affect on crime.

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differs from research at the federal level since we are able to compare actual policy outcomes after

politicians were elected by voters, including over multiple terms. Another benefit of studying cities

is the large number of such governmental units in the United States, since it facilitates the estimation

of econometric models that use appropriate treatment and control groups.

Comparisons between cities with Democrat and Republican mayors find only negligible

differences in the composition of local expenditures, including the proportion spent on police and

fire departments. However, there is a substantial difference in the average size of city government

depending upon whether the mayor is a Democrat or Republican, with Democrat mayors presiding

over larger local public sectors. For example, current spending and full-time municipal employment

per capita are about 18 percent higher in cities where a Democrat won the last election. The data

for revenues and taxes raised show only slightly smaller gaps between the two major parties. Naïve

OLS regressions that control for a variety of local traits reduce these unconditional mean differences

by half, but this still could overstate the impact of political partisanship on the size of city

government if the winning political party is endogenously determined. Given the likelihood that

some unobserved factors (e.g., the differential quality of mayoral candidates across parties or the

true underlying political leanings of the voters) are influencing the outcome, we employ a regression

discontinuity design (RDD) approach akin to that in Lee (2003) and Lee and Dinardo (2004) to deal

with the endogeneity issue. More specifically, we compare policy outcomes from cities where

Democrats barely won an election with cities where Democrats barely lost an election. The RDD

estimates tend to be only about one quarter the magnitude of the naïve OLS estimates, and in no

case is the remaining estimated gap in size of government across the parties statistically significant.

Thus, we find no evidence of partisan differences in the scale of city government, nor in the

composition of its spending.

Why are local politics less divisive, at least in terms of the fiscal outcomes we examine? The

answer could be due to institutional difficulties to make changes quickly.4 We empirically rule out

this institutional inertia explanation by comparing outcomes in the final year of the mayor’s term, in

cities with longer terms of office, and in the final year of the (potential) second term, always finding

no significant partisan differences in fiscal results. Another possible explanation is political

weakness. It could be that the mayor’s party is highly partisan, but does not have the political

4 While some political scientists have argued that changes in policy are easier to approve at the local level (e.g., Peterson (1981)), it could be time-consuming to get a recalcitrant city council to ratify new policy or there could be legal constraints requiring popular votes for certain tax or spending changes.

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strength to move policy to its preferred point. We rule out the political weakness mechanism after

documenting a large political advantage to incumbency: Democrats who barely won the last election

are 36% more likely to win re-election than are Democrats who barely lost, and by much larger

margins of victory. Thus, the political strength of the winning party is much greater in the second

term, but we continue to find no differences in fiscal policy outcomes even after the large exogenous

shift in party strength.5 This observed shift also allows us to apply a formal test of the Alesina

(1988) model of policy divergence based on the empirical decomposition of Lee, Moretti and Butler

(2004). Estimates from this formal model lend additional support to the case for a high degree of

convergence in policy space regarding the size of local government.

The lack of partisan impact on local fiscal outcomes is arguably due to two facts regarding

the environments in which city governments operate: (a) that for issues relevant to local

government, the parties’ preferences for policy are closer to one another; and (b) that there is some

force related to Tiebout-sorting leading the parties to adopt moderate positions even if their

preferred policies are relatively extreme. With respect to the first factor, Glaeser and Ward (2006)

report survey results showing clear differences of opinion along partisan lines in the United States

on cultural, but not economic, grounds. Some of the most divisive cultural issues such as abortion

and the place of religion in the public square effectively have been ‘federalized’ and are no longer

subject to much local control, thereby lowering their partisan content at the city government level.

Our estimates on the size of local government and the composition of its spending are consistent

with the view that they primarily are economic matters for which partisan differences are neither

extreme nor passionate, although this is somewhat surprising given that the proper scale of

government has been the focus of debate between Democrats and Republicans for decades.

That the two major parties are behaving as if differences in preferences were non-existent at

the local level also is consistent with the Tiebout (1956) model. Indeed, a long-running debate

within urban economics is ‘whether Tiebout even needs politics’ (Epple and Zelenitz (1981);

Henderson (1985)). The intuition is that if sorting were perfect and everyone was located in a

jurisdiction that provides just the desired public goods, there seems little need for partisanship. Of

course, that stylized outcome does not reflect reality in most places, so the issue is whether Tiebout-

like forces provide a mechanism to constrain partisanship. In theory, it should be more costly for a

5 In addition to Lee (2003), a number of other studies estimate the incumbent effect or the mechanisms leading to the electoral advantage of incumbents. Some important examples in this literature are Alesina and Rosenthal (1989), Snyder (1990), Besley and Case (1995) and Levitt (1996).

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political party not to cater to a representative median voter in an environment in which the residents

of any given community are relatively homogeneous and are highly mobile within a labor market

area in which similar competitive jurisdictions exist. We present evidence consistent with Tiebout-

related forces making it easier for political parties to commit to centrist policies.

Before getting to that, the next section describes the new mayoral elections data set, as well

as the fiscal policy variables used in the empirical analysis. This is followed in section III with a

discussion of our econometric strategy which is based on a well-known model of political

partisanship. Section IV then presents the empirical estimates, and Section V discusses the

mechanisms related to policy convergence at the local level. Section VI concludes the paper.

II. Data Description

II.A. The Mayoral Electoral Survey Data

The mayoral election data used in this paper were collected from a survey sent to all cities

and townships in the United States with more than 25,000 people as of the year 2000. We requested

comprehensive information on the timing (year and month) of all mayoral elections since 1950, the

name of the mayor and 2nd place candidate, aggregate vote totals and vote totals for each candidate,

party affiliation, type of election, and some additional information pertaining to specific events such

as runoffs and special elections.6

Our final data set contains information on 3,496 elections held in 392 cities between 1950

and 2005.7 The first column of Table 1 reports various demographic, economic, and location data

on these cities as of the year 2000. Naturally, we are keenly interested in the representativeness of

our sample, so the remaining columns in this table report analogous information on different

samples of cities. The second column provides data on the universe of municipalities in the United

States. There are well over 35,000 municipalities, and given our 25,000 population cut-off, it is not

surprising that our cities are much larger than the typical jurisdiction in the country.8 Bigger cities

also tend to have better educated households that earn more money and live in more expensive

houses. They also have more minority households, as indicated by the much larger share of the 6 The strengths of this survey compared to other publicly available data are readily evident. The Municipal Yearbook, for example, only records the name of the current mayor for a given year, without specifying the year of election. The International City Managers Association (ICMA) only collects data on type of election and organizational features of cities every five years, without asking any question related to election outcomes. The Census of Governments also collects some information about type of election, as well as data on the race and gender of elected officials. 7 All results reported in this paper are based on data collected through December 2006. Data collection efforts are ongoing, so the sample will be updated periodically. 8 Average population in our final sample is also skewed by a few very large cities. The median population is 51,464.

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African-American population. Regionally, our sample is more heavily weighted towards the West

and South, with there apparently being numerous small towns in the Midwest region that do not

make it into our sample.

Perhaps more relevant is how representative our sample is compared to all municipalities

with more than 25,000 residents in the year 2000. As indicated by the data in the third column of

Table 1, there are 1,644 such cities. However, in several municipalities mayors are appointed by a

city council, while others hire professional managers to run the city. Column 4 contains descriptive

statistics for the 877 cities that directly elect a mayor.9 Our final sample is very similar in

demographic, economic, and geographic terms to this group of cities. From survey responses, we

were able to obtain at least some information on vote totals and candidate names for 57% of the 877

cities that elect mayors by popular vote. Summary statistics for this group of 503 places are

displayed in Column 5. Our final sample of 392 cities, which is 45% of those places that directly

elect a mayor, also contains information on party affiliation, not just vote totals.

Two factors made it difficult to collect information on candidates’ party affiliation even

when we knew who they were and how many people voted for them. Some cities and counties

could not provide the data because it required gathering information from inaccessible voter

registration records. More importantly, there is a large fraction of cities that are institutionally non-

partisan in that they prohibit party labels from being printed on election ballots or used in election

campaigning. Fifty-nine percent of the cities in our sample are officially non-partisan in this sense.

While this certainly does not mean that nearly 60% of mayoral races literally had no partisan content,

it does signify a major difference with state and federal elections. Indeed, a quick review showed

that elections in many such cities (e.g., Los Angeles, CA) clearly were partisan in the standard use of

that term. Hence, we decided to complement the survey information with on-line searches for party

affiliation information on candidates in all ‘non-partisan’ cities. 10

Another interesting feature about the nature of mayoral races is the relatively large fraction

of cities with two-year term elections. While 51% of our elections are for 4-year terms, 44% are for 9 The total number of cities that elect a mayor is an estimate that was backed out from three different sources: Census of Governments, ICMA and our own survey. Given that we find several discrepancies between the two external surveys and the results directly obtained from the cities, it is very likely that the total number of such cities is slightly larger than 877, potentially reaching 1,000-1,100 cities. 10 These online searches were conducted by accessing restricted content of local newspapers from News Bank, and also by using search engines such as Google. Party affiliation data was easily found for candidates in larger cities, where the mayoral candidates are popularly known as Democrats or Republicans even though the election is officially non-partisan. We also had greater success in finding this information for jurisdictions in larger urban areas that have a richer stock of information online. Overall, approximately 40% of the party affiliation data for non-partisan cities were found with one of the methods above. The remaining 60% were collected directly from city or county clerks.

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2-years only, with 4% being for 3-year terms. The high proportion of 2-year terms generates a

cyclical pattern in the total number of elections by year, as shown in Figure 1. In addition to this

annual cycle, Figure 1 also documents that the number of observations is growing over time. Three

factors account for this: (a) a number of cities in the sample did not have direct elections for mayor

in the past; (b) some city clerks and county clerks lost their records of historical elections or they

were not able to recover the earlier data from old archives; and (c) our on-line searches for party

affiliation had a higher success rate for recent decades. While this means that we work with an

unbalanced panel, this feature of the data is not a concern for the research design used in the

analysis below.

With respect to party affiliation, 51% of the winners in our sample were Democrats, with

40% being Republicans. Over time, however, the proportion of Republican mayors has been

increasing as is documented in Figure 2. This plot of party affiliation since 1950 shows that

Democrats were twice as likely to be in power than Republicans from mid-1950’s through the late-

1970’s. However, the fraction of Democratic mayors has fallen from 60% to 42%. This was

primarily due to Republican gains, as their share of mayoral office in our sample rose from about

25% in the mid-1950’s to 41% in 2005. It is also the case that this sharp reduction in the

Democratic-Republican gap was accompanied by an increase in the proportion of independent

mayors or mayors from other parties.

II.B. Local Public Finance Data

We also collected information on a variety of local public finance variables that are merged

with our elections data. The public finance data span the years 1970-2004 and are from the Historical

Data Base of Individual Government Finances. These data are based on a Census of Governments conducted

every five years, from Annual Survey of Governments collected at every non-census year, and are

complemented with state data provided by the Census Bureau. The local public finance variables

include measures of revenues and taxes, spending (on current operations and capital goods),

employment (full and part time), as well as distributional data regarding shares of spending on labor,

public safety, and parks and recreation.

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The first column of Table 2 reports summary statistics on eleven different variables.11 The

cities in our sample raise and spend just below $1,400 per capita on average.12 The rough equality of

these measures is not surprising given that localities generally are subject to balanced budget

requirements. Taxes correspond to 60% of the overall revenues raised, indicating there are plentiful

transfers from higher levels of government, along with numerous fees not counted as taxes. Most

spending is on current operations, with less than a third on capital items. There are almost 13 full-

time employees per 1,000 residents and just over half of all spending goes towards salaries and wages

in our sample of cities. Public safety in the form of police and fire departments receives over one

quarter of all spending, with parks and recreation receiving less than ten percent on average.

Naive partisan differences in local public finance outcomes are reported in the second and

third columns of Table 2. Unconditional differences between the parties are reported in column 2,

with the conditional differences based on an OLS regression controlling for a number of local traits

in column 3.13 In all cases, a positive value implies that Democrat mayors are running cities with

‘more’ of a particular public finance activity, and the size of government measures are converted to

logs so the numbers represent the proportional differences between Democrats and Republicans.

Cities in which a Democrat won the last mayoral election on average raise more revenues, both tax

and spend more, and have more public employees, with conditional differences ranging from 6-10

percentage points. While the overall size of local government is bigger if the mayor is a Democrat,

there is no evidence of partisan differences regarding how resources are allocated across functions.

All the unconditional differences reported in the bottom panel of Table 2 on the percentage of

spending on police, fire, and parks and recreation are approximately one percentage point or less,

and these become more miniscule after controlling for local traits. In addition, none of those

differences are statistically significantly different from zero.

11 The total number of elections in this final sample is 1,796 because we eliminate the elections with third party mayors since our goal is to compare differences in policy outcomes from Democrats and Republicans. Similarly, we do not use elections where the first and second place candidates were from the same party. Also, the most recent election for each city is discarded since we cannot observe the electoral outcome for election t+1 in such cases. [Being able to estimate partisan effects consistently over multiple terms is an important feature of our empirical strategy. See below for more on that.] Finally, we only use elections data from 1970-2004 because this is the period over which the local public finance data are available. 12 All monetary variables are reported in 2000 dollars. 13 In addition to an indicator variable for whether the mayor is a Democrat, the local controls include city population, the type of election (partisan versus non-partisan, length of term status), median income, percentage of white households, percentage of households with a college degree, the homeownership rate and median house value. Year and region fixed effects also are included. See the notes to the table for the details.

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We interpret these initial descriptives as signaling that partisanship is less intense at the local

level. Crime may get a lot of play on the nightly news, but Democrats and Republicans seem to

agree on how much of the city budget should be spent on public safety. The same holds true for

parks and recreation, and we suspect, for most other government functions handled at the local

level. That said, the OLS estimates also show that the aggregate size of city government--whether

measured in terms of revenues or taxes raised, expenditures made, or employees hired--differs by

party, with local government being larger if the city has a Democrat as mayor. However, a more

appropriate modeling strategy and research design is needed to account for the potentially serious

endogeneity issue that plagues the measurement of partisan differences in local public finance

outcomes. The next section outlines that strategy.

III. Background and Modeling Strategy

The inspiration for economic analysis of political parties dates back at least to Hotelling’s

(1929) famous model of spatial competition. While his framework of a ‘city on a line’ was intended

to explain the central location of firms in physical space, Hotelling himself mentions its applicability

for understanding the tendency of the Democrat and Republican parties to move toward similar

policy positions (on tariffs at the time he wrote). Downs (1957) expanded upon Hotelling’s

conjecture, building a more formal and elaborate structure with rational voters and political parties.

Importantly, the parties cared only about winning elections, and the probability of winning was

maximized if they moved to the center of policy space and captured the median voter.14 In Downs’

framework, democracy and the median voter forced the parties to offer similar platforms, so that the

impact of political partisanship on policy outcomes was nil.

Downs’ convergence result had a powerful influence and became intertwined with the

development of median voter models in the fields of urban economics and political economy.15 The

stark result that partisan politics does not affect policy outcomes at all left many uneasy, and much

effort has been made to amend it. Even before Downs wrote, Smithies (1941) challenged

Hotelling’s pure central location result by pointing out that it relied on an assumption of perfectly

inelastic demand at all locations. In that scenario, moving to the center did not cost anything in

14 Downs’ (1957) dealt with a variety of other matters pertaining to the way democracy worked, including offering predictions on how two versus three (or more) party systems would function. In this paper, we are only concerned with his implications for the effects (or lack, thereof) of political partisanship in a two-party system. 15 Technically speaking, convergence and median voter theorems are not one and the same, as the former is more general than the latter. For our purposes, we can treat them as the same without confusing the interpretation of any of our empirical results.

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terms of lower demand on the fringes of the market. However, if demand was elastic, moving away

from the edge could be so costly that it was not optimal to locate in the middle of the space. In the

language of politics, passionate voters on the extremes might be lost from a move to the center.

Much recent work has more formally introduced passion or ideology of the parties and the

candidates themselves into the analytical framework.16 Intuitively, if a party cares about policy

outcomes, not just being elected, locating in the center of policy space may not maximize the utility

of its members. However, Alesina (1988) showed that incomplete convergence was about more

than whether the party or the candidate cared about something other than being elected. In many

contexts, complete convergence is not dynamically consistent because commitments to centrist

policies by the political parties are not credible. And, if parties cannot credibly commit to moderate

policies, then they will diverge in policy space.

This is most easily understood in the context of a simple one-shot electoral game. In that

situation, the only time consistent equilibrium is one in which the parties follow their own policy

preferences rather than converging on the preferences of the median voter. If parties have an

incentive to announce a moderate policy platform to raise the probability of election, rational,

forward-looking voters will take that incentive into account, rendering the initial commitment non-

credible and leading each party to announce it will implement is own policy preferences upon

winning. While it is possible to get convergence in more complex settings, Alesina (1988)

demonstrated that the ability to credibly commit is an essential underpinning of the traditional

Downs (1957) model of the median voter and political convergence. Alesina’s model serves as the

foundation of our empirical work, so we describe it in the next subsection.

III.A. A Model of Credibility and Policy Convergence

Because Alesina’s (1988) model also has recently been used by Lee, Moretti, and Butler

(2004) to establish the theoretical foundation for an empirical study of the effects of political parties

on U.S. congressional voting behavior, we only briefly outline its basic structure and discuss how its

key comparative static results relate to our empirical estimation. Alesina (1988) models the problem

as a bargaining game between two parties, here denoted as Dems and Reps, that have preferences

over a single policy outcome, S, representing the size of the local government. Each party’s utility

16 In economics, Wittman (1977, 1983) is an excellent example of this subset of the literature on political partisanship. There is much in political science, too, but space limitations prevent us from cataloguing or reviewing that work. See the references in Wittman (1983 especially) and in Besley and Case (2003) for more detail.

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function is concave in the policy outcomes, and the parties’ preferred policy outcomes or bliss

points are different from one another (and exogenously determined).17 The Dems’ bliss point is

defined as s and the Reps is 0, without loss of generality.

Elections are held at the beginning of each period t. Each party announces its policy position

just prior to the election. These are denoted xa and ya, respectively, for the Dems and Reps. Voters

are forward-looking and form rational expectations regarding what policies actually will be

implemented. These expectations are formed prior to the election when the outcome is uncertain,

and are denoted as xe and ye.

The probability that the Dems will win is common knowledge and is given by P, with

P=P(xe, ye). In this framework, each voter favors the party closest to his own bliss point and there is

uncertainty about the true distribution of voter preferences so that the bliss point of the median

voter is not known with certainty. There is an advantage to moving toward the other party in policy

space, potentially attracting voters with preferences in between the parties, so ∂P/∂xe < 0 and ∂P/∂ye

< 0 if xe > ye.

The efficient frontier of outcomes is given by x*=y*=λs, where λ ranges in value from 0 to 1

and represents the weight of the Dems in the bargaining process. Note that if λ=1, the chosen

policy is identical to the Dems’ preferred size of government. In addition, as long as both parties

have concave preferences in the policy outcome, they will prefer a moderate policy with certainty to

the electoral win probability weighted-sum of the outcomes s and 0.

The three possible equilibria from this model are full convergence, full divergence, and

partial convergence. Full convergence is the Downs’ outcome in which the Dems and Reps

announce the same moderate policy and voters expect them to carry out that policy. The latter

requires the commitment be credible and Alesina (1988) discusses conditions such as low discount

rates (which make the parties ‘farsighted’) that could render commitments believable by the voters.

Below, we discuss other mechanisms related to Tiebout sorting that also could increase credibility.

In this case, the important comparative static results are dx*/dP* = dy*/dP* = (dλ*/dP*)s > 0, with P*

representing the underlying probability the Dems would win at the party bliss points s=xe and 0=ye.

An increase in P* reflects an exogenous increase in the political strength of the Dems, so that their

bargaining power is greater and the equilibrium moves closer to their preferred policy position.

While the Dems obviously prefer a higher P*, this should not be confused with them determining

17 The model does not distinguish between a party and its candidates. Similarly, we will not analyze the process by which parties choose a given candidate to run in the general election.

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the relevant policy outcome in the sense discussed above. Voters are in control here in a classic

Downsian sense, as that is what it means when dx*/dP*>0 and dy*/dP*>0.

The second possible outcome is partial convergence, and it implies that 0 < y* < x* < s. In

this scenario, it is still the case that dx*/dP*>0 and dy*/dP*>0. The intuition is that voters can

affect policy to some degree, but are unable to force full convergence.

Full divergence is the last possible outcome, and it occurs when x*=s and y*=0. That is, the

parties implement policies consistent with their bliss points if elected, and voters expected them to

do just that. This equilibrium occurs when it is impossible to credibly commit to moderation

relative to one’s preferred position. In this case, an exogenous increase in the Dem’s political

strength has no effect on the equilibrium so that dx*/dP* = dy*/dP* =0. Voters do not affect the

size of government here. The parties determine that, with the voters simply electing one of parties’

bliss points.

Empirically, the clearest distinction will be between full divergence and the other two

outcomes. This is a test for whether dx*/dP* and dy*/dP* are strictly positive or whether they equal

zero. While recent researchers dismiss the possibility of full convergence and focus on

differentiating between full divergence and partial convergence, we will take the former outcome

more seriously.

III.B. An Empirical Model

Because we only observe the policy outcome associated with the winning party, the main

economic outcome studied in this paper - size of government S - must be written as,

(1) St = Dtxt + (1-Dt)yt,

where Dt is a dichotomous dummy variable indicating whether the Dems won the mayoral election

in period t. To parameterize the key comparative statics, dx*/dP* and dy*/dP*, we follow Lee,

Moretti, and Butler (2004) and rewrite (1) as

(2) St = α + π0Pt* + π1Dt + εt

with the residual ε capturing the possibility that bliss points can vary across cities. An analogous

equation applies for S in period t+1. The variable Pt* represents the probability of victory assuming

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the fixed policy platforms represented by s and 0, so that the estimated coefficient π0 measures the

impact of an increase in the Dems’ political strength. An estimate of π0=0 implies the full

divergence outcome noted above; π0>0 implies some amount of convergence. Note that this

coefficient is estimated controlling for the pure effect of party, which is captured by the π1 term.

Since econometricians do not observe the underlying popularity of the Democrat party as

represented by P*, equation (2) cannot be estimated directly via OLS. However, if there is

exogenous variation in whether the Dems win, a set of individual equations can be estimated which

allow us to identify all the relevant factors.18 For example, the pure party effect (or π1) from

equation (2) can be determined by estimating the average treatment effect in period t :

(3) E{St│Dt=1} – E{St│Dt=0} = π1.

This is the expected difference in the size of local government depending on whether the Dems or

Reps win the mayor’s office. If preferences for policy between Democrats and Republicans are

different, and if they are actually able to implement their preferred policies during the mayoral term,

then π1 should be different than zero. Thus, a positive estimate of π1 is the first and most important

indicator that Democrats and Republicans have differing preferences for policy.

A similar average treatment effect can be estimated for period t+1:

(4) E{St+1│Dt=1} – E{St+1│Dt=0} = π0(P*D,t+1 – P*

R,t+1) + π1(PD,t+1 – PR,t+1) = ψ

where PD,t+1 represents the equilibrium probability of a victory by the Dems in period t+1 given that

they held the mayor’s office in period t , while PR,t+1 is defined analogously but with a Republican

mayor holding office in period t. Equation (4) says that the difference in size of local government

that occurs after the next election, depending upon whether Dems or Reps won the previous

election, can be decomposed into effects due to voters forcing the parties to offer moderate

positions (the π0(P*D,t+1 – P*

R,t+1) component) and those due to purely partisan political differences

(the π1(PD,t+1 – PR,t+1) component). The left-hand side of (4) is directly observable by comparing

policy outcomes between Republicans and Democrats after election t+1, and π1 can be estimated as

in (3), but we still need an estimate of (PD,t+1 – PR,t+1) in order to back out the pure partisanship

component. 18 The source of exogenous variation comes from the comparison of close elections, as discussed below.

13

The term (PD,t+1 – PR,t+1) can be thought as the ‘incumbent effect’, which is defined as the

difference in the equilibrium probability that the Dems will win the next mayoral election (in period

t+1) depending upon which party won the current election. Another way to think about the

incumbent effect is that in addition to policy implications, holding the office during a term will also

potentially lead to electoral gains at the end of the term. More formally, this electoral advantage

from incumbency can be written as:

(5) E{Dt+1│Dt=1} – E{Dt+1│Dt=0} = PD,t+1 – PR,t+1,= γ

The product of the pure party effect and the incumbent effect, π1(PD,t+1 – PR,t+1), measures the

extent to which political parties directly affect policy outcomes via their own preferred positions. As

such, it is a reflection of policy divergence. The extent of policy convergence π0(P*D,t+1 – P*

R,t+1), or

of Downsian-type forces, cannot be observed directly but it can be computed as a residual in

equation (4).19

III.C. Estimation Design

The fundamental identification problem in generating unbiased estimates of the pure party

effect π1 in equation (3) arises from the likelihood that whether or not a Democrat leads a given city

is determined by local traits that are unobserved by the econometrician. In order to overcome this

endogeneity issue, we compare cities where Democrats barely won an election with cities where

Democrats barely lost (and a Republican won). Lee (2003) and Lee and Dinardo (2004)

demonstrate that such a strategy provides quasi-random variation in party winners, since for

narrowly decided races, which party wins is likely to be determined by pure chance as long as there is

some unpredictable component of the ultimate vote.20

A key underlying assumption of the RDD approach is that cities in which Democrat mayors

won a closely contested election are similar on average to cities where Republicans were barely

winners. This implies that all observable and unobservable pre-determined features of cities should

be similar among those races, and it represents a powerful validity test of the research design. More

19 We should emphasize that PD,t+1 is different from P*

D,t+1 in these equations. The latter reflects the true electoral strength of the Dems assuming the parties are expected to choose their policy bliss points, s and 0. This is not observed by the econometrician, which is why the entire term π0(P*

D,t+1 – P*R,t+1) must be imputed as a residual.

20 That there is randomness in the outcomes of close elections is supported by the fact that Democrat and Republican incumbents do not win a disproportionately high fraction of these close races. Thus, there is no evidence that incumbents are able to rig close elections. These results are available upon request.

14

specifically, all relevant observed covariates should be continuous for elections decided by narrow

margins of victory. If they are, then it is likely that the unobservables also will be continuous.

The RDD framework can be estimated parametrically or non-parametrically. Hahn, Todd

and Van der Klaauw (2001) focused on identification and non-parametric estimation when the

discontinuity occurs on a continuous variable. Lee (2005) and Lee and Card (2005) showed that the

RDD can also be estimated parametrically, with similar properties to the non-parametric approach.

Because the former approach allows for straightforward hypothesis testing, in this paper we estimate

the following polynomial functional form for equation (3):

(3’) Sc,t = β0 + Dc,tπ1 + MVc,tβ1 + MV2c,tβ2 + MV3

c,tβ3 + Dc,tMVc,tβ4 + Dc,tMV2c,tβ5 +

Dc,tMV3c,tβ6 + ηc,t

where MVc,t refers to the margin of victory in election t in city c (defined as the difference between

the percentage of votes received by the winner and the percentage of votes received by the second

place candidate),21 and St represents the size of local government in the term immediately following

election t (i.e., it is not the size of government on election night). Thus, the pure party effect which

is reflected in the coefficient π1 in equation (3’) is estimated controlling for the margin of victory in

linear, quadratic, and cubic form, as well as interactions of each of these terms with a dichotomous

dummy for whether a Democrat won the mayor’s race in election t in city c.22

A similar RDD will be used to estimate the impact of winning a close election on fiscal

policy outcomes after election t+1, and on the electoral outcomes at the end of the mayoral term,

according to the following equations:

(4’) Sc,t+1 = δ0 + Dc,tψ + MVc,tδ1 + MV2c,tδ2 + MV3

c,tδ3 + Dc,tMVc,tδ4 + Dc,tMV2c,tδ5 +

Dc,tMV3c,tδ6 + νc,t

(5’) Dc,t+1 = λ0 + Dc,tγ + MVc,tλ1 + MV2c,tλ2 + MV3

c,tλ3 + Dc,tMVc,tλ4 + Dc,tMV2c,tλ5 +

Dc,tMV3c,tλ6 + υc,t

21 Margin of victory is used in lieu of the vote share in order to facilitate comparison across elections, as some have more than two candidates because of write-in ballots or independent candidates. Non-partisan elections also can have more than one candidate from the same party. 22 The proper order of the polynomial regression is still open to debate in the RDD literature. Although a quadratic polynomial would fit our data well (see the pictures in the empirical results section), we estimate a 3rd order polynomial given that Porter (2003) argues that odd polynomial orders have better econometric properties.

15

where the total effect of party labels on policy, ψ ,is estimated with the same regressors in equation

(4’), but the size of government is measured in the term immediately following election t+1. The

incumbent effect, PD,t+1 – PR,t+1, which is represented by the coefficient γ in equation (5’) reflects the

increased probability of a Democrat winning the next election assuming a Democrat won the

previous one, also controlling for the third-order polynomial in margin of victory and its interaction

terms.

The comparison of policy outcomes after election t+1 provides an estimate of the causal

effect of exogenously holding the office during the campaign (i.e., after having won election t). As

we saw in the previous section, this overall effect can be decomposed into two parts. The first

component is due to partisan differences in the preferences of each party regarding the size of local

government. Mathematically, this is the product of the incumbent effect, (PD,t+1 – PR,t+1), and the

pure party effect which is reflected in the π1 term in the econometric model above. The larger this

component, the more it is the case that voters are ‘electing’ a policy by picking one of the parties’

bliss points regarding size of government. The second component is that due to Downs-like forces

of convergence in which the desire to capture the median voter drives the parties to adopt moderate

positions (the same position in the extreme). Mathematically, this is the residual from subtracting

the first component from the overall effect which is given by ψ in the econometric model. The

larger is this component, the more it is the case that the voters are ‘affecting’ policy in the sense it is

the median voter’s bliss point, not the political parties’ bliss points, which determine the ultimate

policy outcome.23

The intuition behind this framework is that policy outcomes after election t+1 are also

affected by the exogenous change in electoral strength of the Democrat party associated with

victories in close races. In a Downsian view of the world, an increase in the electoral strength of the

Democrat party would lead them to vote in a more partisan way. On the other hand, if politicians

cannot credibly commit to policies in the sense of Alesina (1988), a shift in electoral strength should

not affect their behavior.

IV. Empirical Results

IV.A. RDD Estimates of the Party Effect on the Size of Local Government

23 The ‘elect’ versus ‘affect’ language was used by Lee, Moretti, and Butler (2004) and we adopt it in some of the discussion below.

16

Regression discontinuity estimates of π1 , the pure party effect on the size of local

government, are reported in Table 3. We present RDD estimates for the four main fiscal variables

with large positive conditional OLS estimates of partisan differences: total revenues per capita, total

taxes per capita, total expenditures per capita and full time employment per 1000 residents. The

results in the first column are based on the estimation of equation (3’), showing the impact over the

term of office immediately following the mayor’s election. These estimates are directly comparable

to the OLS results in column 3 of Table 2 because both represent differences in size of government

between Democrat and Republican mayors that are elected in time t. The RDD coefficients are no

more than 25%-50% of the magnitude of the conditional OLS estimates, and none is significantly

different from zero. This suggests that unobserved factors were driving a great deal of the OLS

differences in policy outcomes.

Because pictures can be very illuminating in a regression discontinuity context, Figure 3

graphs the results from this regression for the same four measures of government size. The top left

panel depicts the findings for log total revenues per capita. Each dot in the panel corresponds to the

average log total revenues per capita during the term that follows election t, given the margin of

victory obtained by Democrats in election t. The solid line in the figure represents the predicted

values from the polynomial fit described in equation (3’), with the dashed lines identifying the 95%

confidence intervals. Only results for the Democrats are presented because the Republicans are the

mirror image. Although we observe an overall positive relationship between total revenue per capita

and the margin of victory for Democrats – just as in the OLS estimates – there is no meaningful

discontinuity around the cut off for close elections. Similar patterns are documented in the other

panels of Figure 3 for the other measures of government size.

The second column of Table 3 reports results from a specification that includes the lagged

value of the dependent variable for the year prior to election t. The intuition here is that including

this pre-determined feature in the RDD equation might control for some of the noise observed in

Figure 3. The standard errors are lower, sometimes by 50% or more, and the RDD point estimates

continue to indicate no meaningful impact of political partisanship on the size of government.

Similarly, the results reported in the third column of Table 3 show that there is no material

difference in outcomes for the last year of the mayor’s term compared to the average effects over

the entire term. While the absence of larger partisan effects later in the term of office argues against

the difficulty of implementing institutional change as an explanation for the absence of partisan

17

impacts, that issue is dealt with more extensively below when we analyze the influence of party in

the subsequent term of office.24

These findings are in stark contrast to the host of estimates from the studies discussed above

that focus on behavior at the state and federal levels. If taken at face value, they indicate that local

politics are much less partisan than national or state politics. However, there are other potential

explanations that warrant discussion and analysis, including political weakness preventing a party

from behaving in a preferred partisan manner and the possibility that it takes more than one term to

implement fiscal changes of the type we are examining. To gain insight into those issues, we first

estimate the change in political strength due to incumbency.

IV.B. The Incumbent Effect and Changes in Political Strength

It could be that the mayor’s party is highly partisan, but does not have the political strength

to move to its bliss point. After all, we are identifying π1 off the variation from elections with very

tight margins of victory. However, estimation of equation (5’) finds a very large value for γ, which

represents the incumbent effect (the PD,t+1 – PR,t+1 term in our model). The RDD point estimates of

γ are reported in the first row and column of Table 4 for our full sample, with Figure 4 plotting the

underlying data from this specification. Each dot corresponds to the Democrat party probability of

victory in election t+1 given the margin of victory obtained by Democrats in election t. As

expected, the slope of this relationship is positive, signaling that the larger the margin of victory in

the current election, the greater the probability of winning the next election. More importantly, this

relationship clearly is not continuous. When Democrats barely win an election, they have about a

70% chance of winning the next election. If they barely lost in election t, they win only 35% of the

time in the subsequent election. The difference between both outcomes is the causal incumbent

effect - about 35 percentage points as depicted in Figure 4.25

Figure 5 then documents that this change in political strength is also reflected in the margin

of victory in the next election. Democrat mayors who win election t by a very small margin go on to

win in election t+1 by a margin of about 10 percentage points. In contrast, if the Democrats barely

lost election t, they tend to suffer a heavy penalty in the subsequent election, losing by about 20

24 The absence of a pure party effect is also observed for selected subsamples of the data, such as elections in larger cities, officially partisan elections, elections to four year terms, and elections in the 1990s. Results for these groups are analogous quantitatively and qualitatively to those in the first column of Table 3, although they naturally are less precise because of the reduced number of observations. These estimates are available upon request. 25 Regressions not reported here show this large incumbent effect almost does not vary much by type of election (partisan vs non-partisan), by size of the city and for the most recent elections.

18

percentage points. The full gap is about 28 percentage points, as reported in the second row of the

first column of Table 4. Thus, incumbency conveys significant political advantage to a political

party, both in terms of the probability of winning the next election and in terms of the ease of

winning that election. While our estimate for the probability of winning an election is slightly

smaller than the 38.5 and 47.6 point effects reported by Lee (2003) and Lee, Moretti, and Butler

(2004), respectively, for the U.S. congressional representatives, the impact on margin of victory at

the local level is more than double the estimates observed in those two studies.

IV.C. Formal Test of Political Divergence

Since incumbent office holders have greater political strength in election t+1, we can test if

they are also more likely to implement different policies during the term that follows election t+1.

These results, presented in Table 5, are estimates of the full partisan effect ψ shown in equation (4’).

As in Table 3, results from three specifications are reported. The first two columns are based on

data over the full term of office, with the second also controlling for the relevant fiscal condition in

the year prior to election t. The third only uses fiscal data from the last year of the relevant term.

The results essentially rule out political weakness as an explanation for the lack of partisan effect at

the local level. There is no doubt that political strength is much greater given the large incumbency

effect, but this does not translate into significantly larger government (however measured) in the

second term, as evidenced by the small and insignificant RDD estimates. Figure 6’s graphs visually

highlight the lack of any material discontinuity associated with a close election t.

Statistically, our results are consistent with there being no partisan impact on this local public

outcome. However, the stringent data requirements the regression discontinuity approach imposes

lead us to caution against drawing the stark conclusion that there is absolutely no policy divergence

due to party partisanship. Consequently, we still carry out the decomposition presented in Lee,

Moretti and Butler (2004), taking the point estimates at face value (i.e., ignoring the fact that the

standard errors are such that we cannot reject estimates of zero). Table 6 then reports the

decomposition of our ψ estimate into two components as described in equation (4). The first

column reproduces the total impact estimates from Table 5. Column 2 then provides a calculation

of the divergence component, or π1(PD,t+1 – PR,t+1). Column 3 notes the difference between the total

effect and divergence estimate, which represents the degree of convergence, with the final column

showing the percentage of this effect in terms of the overall impact. All but one of the convergence

shares is two-thirds or more, which is in stark contrast to the almost zero convergence share found

19

by Lee, Moretti and Butler (2004) for voting behavior in the U.S. House of Representatives.

Therefore, even when taking all our coefficients at face value, we find relatively little evidence of

policy divergence for the size of government at the local level.

IV.D. Validity Tests

Our primary identification assumption is that the set of cities where Democrats barely won a

mayoral election are ex ante comparable to cities where Republicans barely won the election. To test

the validity of this assumption, we present evidence on the similarity of four sets of pre-determined

characteristics of cities: demographics, regional location, size of government and previous electoral

outcomes. Table 7 reports all the underlying regression results, but we focus our discussion on the

graphical depictions of our findings.

Figure 7 plots the distribution of four demographic variables by the Democrat party margin

of victory. The upper left panel graphs the results for the average proportion of whites in those

cities. Although a negative relationship between the Democratic margin of victory and percentage

white is evident, this variable does not present any indication of a discontinuity for closely contested

races. Similar patterns are observed for educational achievement (as measured by the fraction of

adults with college degree), median family income, and median house prices in the remaining panels

of Figure 7. The discontinuity tests for population and geographic location are presented in Figure

8. Once again, we do not observe any differential outcome at the cut off point for close elections

for any of these variables.

Figure 9 shows the analogous plots for four pre-determined fiscal outcomes: total revenues

per capita, total taxes per capita, total current expenditures per capita, and total full time employees

per 1,000 residents. Again, they all validate the key identification assumption of continuity for all

observed covariates in cities with closely contested elections. Finally, Figure 10 presents three

previous electoral outcomes: the Democrat probability of winning election t-1, the corresponding

margin of victory in election t-1, and the total number of previous Democrat mayors in a city. There

is no evidence of any discontinuity in these plots, in stark contrast to the incumbent effects depicted

in Figures 4 and 5.

In sum, we can find no discontinuity in any predetermined demographic, political, or

geographic traits of our municipalities. As such, our main identification assumption appears to be

robust.

20

V. Mechanisms and Discussion

The evidence presented here suggests that partisan effects are very limited at the local level.

Given how much at variance that is from recent empirical investigations into the impact of

partisanship at other levels of government, it is important to think about the different conditions

and mechanisms that could justify the differences in results.

One potentially important difference is that local governments typically are situated in urban

labor market areas that may be Tiebout-like in nature. Consider an extreme case in which sorting is

perfect and each city is homogenous in the sense that the residents of any given community have the

same attributes and desire the same things from local government. In that case, it pays for political

parties not to deviate from a ‘moderate’ position – which in this case is the position of the

population in that locale - even if they want a substantially different sized government for partisan

reasons. The virtually certain chance of losing the next election if one deviates from the announced

moderate position is what would make for a credible commitment by a political party. Of course,

this extreme does not exist in reality, but a typical city jurisdiction certainly is more homogeneous

than the typical congressional district, metropolitan area, or state simply because of geographic and

population sizes. If more homogeneity in voter’s traits translates into similar preferences for local

public goods, as seems likely, then deviating very far from the center of the distribution is going to

be very costly to a political party.26

To see whether there is any evidence in support of this hypothesis, we investigated whether

there were partisan differences based on how similar were family incomes within the community.

More specifically, for a large subset of our sample, we were able to compute the coefficient of

variation in family income across census block groups within each community in the year 2000.27

We then divided the municipalities in half and estimated OLS and RDD specifications analogous to

those reported above on each subsample. The first two columns of Table 8 report results for the

relatively homogenous cities with coefficients of variation of family income below the sample

26 In addition, Tiebout sorting also implies competition among several relatively homogenous jurisdictions within labor market areas. If a mayor does not cater to the median voter, the city faces the risk of losing population to neighbor municipalities. In the near term, this may increase the chances of re-election since dissident voters are moving out, but it also brings with it the potential for house values and tax revenues to decline, especially if new citizens are not attracted immediately. How land prices will adjust across all communities is not easy to determine in the absence of a complex urban model, but competition among municipalities for residents should provide an incentive for mayors not to deviate from moderate policies (as defined by the relevant median voter) when moving costs are low. 27 The coefficient of variation is based on the standard deviation of all block group average incomes in a city divided by the average income in each city. Census block groups are regions with approximately 1,000 people.

21

median value for that statistic; the final two columns report the analogous data for the more

heterogeneous places with relatively large variation in mean income levels across census tracts.

The conditional OLS partisan differences always are higher (sometimes by a large amount) in

the cities with relatively high income variability across tracts (e.g., compare columns 1 and 3 of Table

8). The RDD estimates for the more homogenous communities always are small and never

statistically significant (column 2). However, the RDD results for the more heterogeneous places in

terms of income are much larger on average and some are statistically greater than zero at standard

confidence levels (column 4). The general pattern of these findings is quite consistent with what

one would expect if Tiebout-like forces were relevant in restraining the effects of political

partisanship.

Since local municipalities are more homogeneous, local politicians are probably more likely

to have more similar preferences for policy. That even large shifts in the political strength of a party

do not lead to changes in policy suggest that the two national parties’ preferences at the local level

are less far apart from each other than at the state or national level. It is also possible that the

greater convergence in the policy space we have focused upon - size of government and

composition of local expenditures - occurs because political partisanship along these lines is less

intense than for other issues. Glaeser and Ward (2006) argue that the real differences in partisan

beliefs in recent decades are along cultural, not economic, lines. If the size of city government

primarily is an economic matter, then it may be easier to compromise because partisan differences

are not all that great in the first place.

Another distinctive feature of the local political environment is that there does not appear to

be any viable mechanism by which a state or national party leader could pressure a mayor to deviate

from policies desired by the median voter in the community. For legislators at the state or federal

level, the party leader has a potentially powerful enforcement mechanism through the ability to

decide on the assignment to committees of relevance to the legislator’s district or the allocation of

campaign funds from the state or national party.28 Not only is this type of mechanism absent for

mayors, but the fundamental Tiebout-like forces in effect at the local level probably would lead to

less use of such a mechanism were it available.

VI. Conclusion

28 See Snyder and Groseclose (2000) for an example of the influence of the party whip in a legislative setting.

22

This is the first direct study of the impact of political parties at the local level in the U.S. It

relies on information from a new panel data base of mayoral elections between 1950 and 2005. A

host of recent empirical findings concludes that political partisanship is playing an important role in

determining various policy or economic outcomes at the state and federal levels of government.

That is, the parties themselves, not the electorate through the preferences of the median voter, are

effectively determining policy. We find virtually the opposite result when we investigate the role of

partisanship at the local level, using various measures of local allocation of resources and of size of

city government. Controlling for the endogeneity of which party wins the mayor’s office in local

elections with a regression discontinuity design that relies on the quasi-experimental variation from

closely contested races, we find that local fiscal outcomes are virtually the same regardless of

whether a Democrat or Republican becomes mayor. More formal decompositions of the impact of

partisanship also conclude that there is substantial convergence in policy space at the local level.

That partisanship does not always dominate suggests that future research in this area focus

more closely on identifying the mechanisms and conditions that mediate its influence at all levels of

government. The impacts of political parties on policy outcomes appear to be malleable. Our study

suggests that similar party preferences and spatial sorting into relatively homogenous groups provide

conditions that help parties make credible commitments, but it may not be feasible (or even

desirable) to create a similar environment for other levels of government. Finally, future research

should try to expand beyond our analysis of the size of government and the allocation of resources

to policies such as zoning laws and the attraction of new business, among others. It is possible that

the two major political parties may have different views of other aspects of the local economy, but

we leave such investigation for future empirical work.

23

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25

Table 1. Sample Representativeness cities cities with cities >25000,

with >25000 >25000, and elected mayor,andfinal sample all US cities population elected mayor survey response

observations 392 35660 1644 877 503

population 125,501 6457 87821 112392 114,000(261497) (58103) (256860) (346409) (234973)

% west 0.19 0.06 0.23 0.18 0.21(0.39) (0.24) (0.42) (0.39) (0.41)

% south 0.24 0.18 0.21 0.25 0.29(0.42) (0.38) (0.41) (0.44) (0.45)

% north 0.17 0.17 0.22 0.23 0.13(0.37) (0.38) (0.42) (0.42) (0.34)

% white 0.69 0.89 0.70 0.69 0.68(0.22) (0.18) (0.23) (0.23) (0.22)

% black 0.13 0.04 0.11 0.13 0.12(0.18) (0.13) (0.15) (0.17) (0.17)

% college degree 0.26 0.16 0.28 0.26 0.27(0.13) (0.12) (0.14) (0.13) (0.13)

median family income 53,232 45574 56790 53334 53,478(16760) (17560) (18772) (16687) (16669)

median house value 132,910 89466 151817 133838 135,065(69127) (76311) (99470) (70988) (69964)

Notes: All variables are based on the 2000 Census. Column 1 presents descriptives for the final mayoral election sample used in this paper. Column 2 reports descriptives for all cities in the US. Column 3 restricts the sample to cities with more than 25,000 people as of year 2000. Column 4 additionally constrains the sample to cities that directly elect a mayor. Column 5 present results for cities that replied to the survey with vote totals but no information about party affiliation. See the text for other details.

26

Table 2. Local Public Finance Variables Descriptive Statistics and OLS Estimates of Differences between Democrat and Republican Cities

baseline OLS OLSvariables mean unconditional conditional

size of governmenttotal revenues per capita 1370 0.152 0.087

(807) (0.032) (0.026)

total taxes per capita 838 0.115 0.066(651) (0.036) (0.028)

total expenditures per capita 1380 0.151 0.088(830) (0.032) (0.027)

current expenditures per capita 999 0.176 0.096(663) (0.035) (0.028)

investments and construction per capita 362 0.066 0.039(287) (0.044) (0.040)

full time employment per 1000 residents 12.53 0.180 0.086(8.04) (0.037) (0.028)

partial time employment per 1000 residents 2.27 0.072 0.057(2.12) (0.055) (0.049)

allocation of resourcespercentage spent with salaries and wages 0.506 0.012 0.007

(0.119) (0.007) (0.006)

percentage spent with police department 0.174 -0.012 -0.004(0.080) (0.005) (0.004)

percentage spent with fire department 0.109 -0.006 -0.004(0.050) (0.003) (0.003)

percentage spent with parks and recreation 0.079 -0.010 -0.004(0.063) (0.004) (0.003)

observations 1796 1796 1796

% diff Dems and Reps

Notes: Columns 2 and 3 report coefficients from an OLS regression of the independent variables indicated in the table on an indicator variable for whether the mayor is a Democrat. All size of government variables were transformed to logs in the OLS regressions. Local controls used in column 3’s specification include city population, the type of election (partisan versus non-partisan, length of term status), median income, percentage of white households, percentage of households with college degree, homeownership rate and the median house value. Year and region fixed effects also are included. See the text for a more detailed explanation of the political and fiscal variables. Reported standard errors are based on clustering by city and decade.

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Table 3. RDD Estimates of the Differences in Size of Government Measures between Democrat and Republican Mayors

average of average of last yeardependent variables the term the term of the term

total revenues per capita 0.020 -0.021 -0.023(0.054) (0.028) (0.032)

total taxes per capita -0.008 0.006 0.012(0.057) (0.033) (0.035)

total expenditures per capita 0.021 -0.004 0.006(0.056) (0.027) (0.032)

full time employment per 1000 residents 0.045 0.007 0.011(0.056) (0.021) (0.025)

lagged value of the dependent variable NO YES YEScovariates YES YES YESobservations 1796 1715 1715

Notes: The table presents RDD coefficient estimates of each fiscal policy outcome on an indicator for Democrat victory in election t as described in equation (3’) in the text. All size of government variables were transformed to logs. All regressions also include a cubic polynomial on margin of victory, and interactions of these polynomials with the indicator for Democrat victory. The lagged value corresponds to the fiscal variable in the year prior to election t. Covariates include city population, the type of election (partisan versus non-partisan, length of term status), median income, percentage of white households, percentage of households with college degree, homeownership rate and the median house value. Year and region fixed effects also are included. Reported standard errors are based on clustering by city and decade.

Table 4. RDD Estimates of the Democrat Incumbent Effect

dependent variables RDD

Democrat probability of win, t+1 0.358(0.056)

Democrat margin of victory, t+1 0.277(0.049)

covariates YESobservations 1796

Notes: The table presents RDD coefficient estimates of each electoral outcome on an indicator for Democrat victory in election t as described in equation (5’) in the text. All regressions also include a cubic polynomial on margin of victory, and interactions of these polynomials with the indicator for Democrat victory. Covariates include city population, the type of election (partisan versus non-partisan, length of term status), median income, percentage of white households, percentage of households with college degree, homeownership rate and the median house value. Year and region fixed effects also are included. Reported standard errors are based on clustering by city and decade.

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Table 5. RDD Estimates of the Differences in Size of Government Measures between Democrat and Republican Mayors after Election t+1

average of average of last yeardependent variables the term the term of the term

total revenues 0.033 -0.005 0.031(0.057) (0.036) (0.038)

total taxes -0.010 0.019 0.026(0.057) (0.035) (0.038)

total expenditures 0.034 0.009 -0.011(0.058) (0.036) (0.040)

full time employment per 1000 residents 0.050 0.015 0.027(0.059) (0.022) (0.026)

lagged value of the dependent variable NO YES YEScovariates YES YES YESobservations 1536 1455 1455

Notes: The table presents RDD coefficient estimates of each fiscal policy outcome after election t+1 on an indicator for Democrat victory in election t as described in equation (4’) in the text. All size of government variables were transformed to logs. All regressions also include a cubic polynomial on margin of victory, and interactions of these polynomials with the indicator for Democrat victory. The lagged value corresponds to the fiscal variable at the year prior to election t. Covariates include city population, the type of election (partisan versus non-partisan, length of term status), median income, percentage of white households, percentage of households with college degree, homeownership rate and the median house value. Year and region fixed effects also are included. Reported standard errors are based on clustering by city and decade.

Table 6. Total Partisanship Effect Decomposition (1) (2) (3) (4)

dependent variables

total revenues 0.033 0.007 0.026 78.3%

total taxes -0.010 -0.003 -0.007 71.4%

total expenditures 0.034 0.008 0.026 77.9%

full time employment per 1000 residents 0.050 0.016 0.034 67.8%

total impact (ψ)

divergence Π1(PD,t+1 – PR,t+1)

convergence (2) - (1)

convergence share (3)/(1)

Notes: Column 1 reproduces the results from the first column of Table 5. Column 2 multiplies the incumbent effect in Table 4 and the pure party effect estimated in Table 3. Column 3 subtracts column 1 from column 2. Finally, Column 4 calculates the ratio of column 3 over column 1. For additional details see equation (4) in the text.

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Table 7. RDD Estimates for Pre-Determined Features of Cities

% white % college family income house values

-0.009 -0.013 -1768 -3112(0.023) (0.014) (1742) (8417)

% north % west % south population

0.029 -0.035 -0.107 8102(0.052) (0.046) (0.058) (29628)

total revenues total taxes total expenditures full time employeesper capita, t-1 per capita, t-1 per capita, t-1 per 1000 residents, t-1

0.045 0.001 0.041 0.065(0.054) (0.055) (0.059) (0.055)

Democrat probability Democrat margin Democrat numberof win, t-1 of victory, t-1 of previous mayors

0.044 -0.052 0.004(0.065) (0.049) (0.546)

Discontinuity Estimates for Demographic Characteristics

Discontinuity Estimates for Population and Regional Location

Discontinuity Estimates for Fiscal Outcomes Prior to Election t

Discontinuity Estimates for Previous Election Outcomes

Notes: The table presents RDD coefficient estimates of each variable on an indicator for Democrat victory in the election t. All regressions also include a cubic polynomial on margin of victory, and interactions of these polynomials with the indicator for Democrat victory. All regressions were clustered by city and decade. Table 8. OLS and RDD Estimates By Cities with Low and High Coefficient of Variation in Income

OLS OLSvariables conditional RDD conditional RDD

total revenues per capita 0.053 -0.037 0.138 0.134(0.039) (0.072) (0.036) (0.083)

total taxes per capita 0.045 -0.025 0.089 0.061(0.040) (0.084) (0.032) (0.069)

total expenditures per capita 0.056 -0.044 0.147 0.155(0.040) (0.075) (0.038) (0.087)

full time employees per 1000 residents 0.084 0.007 0.121 0.167(0.040) (0.074) (0.034) (0.077)

observations 855 855 823 823

Homogenous Group Heterogeneous Group

Notes: The homogenous group corresponds to the cities with a coefficient of variation in income below the median. The heterogeneous group corresponds to the cities with a coefficient of variation in income above the median. Regressions follow similar OLS and RDD specifications presented in previous Tables 2 and 3.

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Figure 1. Total Number of Municipal Elections by Year, 1950-2005

Figure 2. Proportion of Democratic and Republican Mayors by Year, 1950-2005

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Figure 3. Local Public Finance Variables by Democrat Margin of Victory in Election t

Figure 4. Democrat Probability of Victory in Election t+1 given Margin of Victory in Election t

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Figure 5. Democrat Margin of Victory in Election t+1 given Margin of Victory in Election t

Figure 6. Local Public Finance Variables After Election t+1 by Democrat Margin of Victory in Election t

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Figure 7. Validity Test I: Demographics

Figure 8. Validity Test II: Population and Regional Location

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Figure 9. Validity Test III: Previous Fiscal Outcomes

Figure 10. Validity Test IV: Previous Electoral Outcomes